相关疑难解决方法(0)

我StandardScaler从之前的模型中保存了一个sklearn ,并尝试将其应用于新数据

scaler = myOldStandardScaler
print("ORIG:", X)
print("CLASS:", X.__class__)
X = scaler.fit_transform(X)
print("SCALED:", X)

我有三个观察,每个观察2000个特征.如果我单独运行每个观察,我得到全零的输出.

ORIG: [[  3.19029839e-04   0.00000000e+00   1.90985485e-06 ...,   0.00000000e+00
0.00000000e+00   0.00000000e+00]]
CLASS: <class 'numpy.matrixlib.defmatrix.matrix'>
SCALED: [[ 0.  0.  0. ...,  0.  0.  0.]]

但是,如果我将所有三个观察结果附加到一个数组中,我得到了我想要的结果

ORIG: [[  0.00000000e+00   8.69737728e-08   7.53361877e-06 ...,   0.00000000e+00
0.00000000e+00   0.00000000e+00]
[  9.49627142e-04   0.00000000e+00   0.00000000e+00 ...,   0.00000000e+00
0.00000000e+00   0.00000000e+00]
[  3.19029839e-04   0.00000000e+00   1.90985485e-06 ...,   0.00000000e+00
0.00000000e+00   0.00000000e+00]]
CLASS: <class 'numpy.matrixlib.defmatrix.matrix'>
SCALED: [[-1.07174217  1.41421356  1.37153077 ...,  0.          0.          0.        ]
[ 1.33494964 -0.70710678 -0.98439142 …